Fueled by recent advances in Deep Learning (DL), the field of neural audio synthesis has shown the potential to overcome some of the inconveniences mentioned above. Many deep generative models can learn high-level latent variables that provide more expressive and intuitive means for sound exploration and synthesis [1]}, [2]}, [3]}. In addition, as DL models can be trained on arbitrary data, the sound diversity is not strictly limited to that of a particular synthesis process. Despite the unprecedented success of many of these methods in experimental settings, just a handful of works have culminated into production-ready tools for music creation (e.g., Mawf,https://mawf.io/ Neurorack [4]}). The challenge of modeling high-quality raw audio at scale [5]}, coupled with the requirement for such music creation tools to operate in real-time in resource-limited environments,
has remained the thorn in the side for their use in commercial applications.
